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Information recovery in behavioral networks.

Tiziano Squartini1, Enrico Ser-Giacomi2, Diego Garlaschelli3

  • 1Istituto dei Sistemi Complessi, Universitá di Roma "Sapienza", 00185 Rome, Italy.

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Summary
This summary is machine-generated.

This study introduces novel information-theoretic methods for network tomography, recovering agent behavior from origin-destination data. The approach uses entropy maximization on networks to predict choices analytically without simulation.

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Area of Science:

  • Agent-based modeling
  • Network theory
  • Information theory

Background:

  • Recovering behavior-related choice information from origin-destination data is crucial for understanding complex systems.
  • Existing methods often rely on explicit sampling, which can be computationally intensive.
  • Adaptive intelligent behavior and self-organization in dynamic systems are key factors in agent choices.

Purpose of the Study:

  • To develop analytical, information-theoretic methods for network tomography.
  • To recover unknown behavioral flow parameters from origin-destination data.
  • To connect adaptive behavior, entropy maximization, and self-organization.

Main Methods:

  • Casting the problem in terms of binary and weighted networks.
  • Employing entropy-driven methods based on the Cressie-Read family of entropic functionals.
  • Utilizing Shannon and likelihood functionals for analysis.

Main Results:

  • Analytical recovery of unknown behavioral values across ensembles without explicit configuration space sampling.
  • Demonstrated application to univariate and bivariate datasets.
  • Comparison of the accuracy of different entropic functionals in reproducing observed trends.

Conclusions:

  • Information-theoretic approaches, particularly entropy maximization, provide powerful tools for network tomography.
  • The Cressie-Read family of functionals offers a flexible framework for estimating behavioral parameters.
  • Analytical methods can efficiently recover behavioral insights from origin-destination data.